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plot_Nodes_routing_UMass.py
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plot_Nodes_routing_UMass.py
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import numpy as np
import matplotlib.pyplot as plt
time_epochs = 10
runs = 2
Xchants = np.zeros(shape=(time_epochs, runs))
Xchants_pk = np.zeros(shape=(time_epochs, runs))
Epidemic_ALL = np.zeros(shape=(time_epochs, runs))
Epidemic_LTE = np.zeros(shape=(time_epochs, runs))
Epidemic_TV = np.zeros(shape=(time_epochs, runs))
Epidemic_CBRS = np.zeros(shape=(time_epochs, runs))
Epidemic_ISM = np.zeros(shape=(time_epochs, runs))
SprayNWait_ALL = np.zeros(shape=(time_epochs, runs))
HotPotato_ALL = np.zeros(shape=(time_epochs, runs))
T = 180
startTime = 840
num_messages = 300
days = [ "2007-11-07", "2007-11-06"]
folder_nums = [x for x in range(1,11, 1)]
bands_epi = ["ALL", "LTE", "TV", "CBRS", "ISM"]
bands = ["ALL"]
protocols = ["XChant", "Epidemic", "SprayNWait", "HotPotato"]
metrics_file = "metrics.txt"
p_id = 2 # p_id = 1 for PDR, = 2 for latency, and 3 for Energy, and 4 for overhead
t = 0
for num_mules in folder_nums:
for i in range(len(days)):
for protocol in protocols:
if protocol == "Epidemic":
for band in bands_epi:
path = "DataMules/UMass/" + days[i] + "/1/Link_Exists/LE_" + str(startTime) + "_" + str(T) + "/" + protocol + "/" + band + "/" + str(num_mules) + "/" + str(num_messages) + "/"
with open(path + metrics_file, "r") as f:
lines = f.readlines()[1:]
for line in lines:
line_arr = line.strip().split()
if "180" in line_arr:
if band == "ALL":
Epidemic_ALL[t][i] = float(line_arr[p_id])
elif band == "LTE":
Epidemic_LTE[t][i] = float(line_arr[p_id])
elif band == "TV":
Epidemic_TV[t][i] = float(line_arr[p_id])
elif band == "CBRS":
Epidemic_CBRS[t][i] = float(line_arr[p_id])
elif band == "ISM":
Epidemic_ISM[t][i] = float(line_arr[p_id])
else:
for band in bands:
path = "DataMules/UMass/" + days[i] + "/1/Link_Exists/LE_" + str(startTime) + "_" + str(T) + "/" + protocol + "/" + band + "/" + str(num_mules) + "/" + str(num_messages) + "/"
with open(path + metrics_file, "r") as f:
lines = f.readlines()[1:]
for line in lines:
line_arr = line.strip().split()
if "180" in line_arr:
if protocol == "XChant":
Xchants[t][i] = float(line_arr[p_id])
elif protocol == "SprayNWait":
SprayNWait_ALL[t][i] = float(line_arr[p_id])
elif protocol == "HotPotato":
HotPotato_ALL[t][i] == float(line_arr[p_id])
t += 1
t = 0
for num_mules in folder_nums:
for i in range(len(days)):
for band in bands:
path = "DataMules/UMass/" + days[i] + "/1/Link_Exists/LE_" + str(startTime) + "_" + str(
T) + "/XChant/" + band + "/" + str(num_mules) + "/" + str(num_messages) + "/"
with open(path + "metrics_opt.txt", "r") as f:
lines = f.readlines()[1:]
for line in lines:
line_arr = line.strip().split()
if "180" in line_arr:
Xchants_pk[t][i] = float(line_arr[p_id])
t += 1
if p_id == 3:
for t in range(len(Xchants)):
for run in range(runs):
Xchants[t][run] = float(Xchants[t][run]) / 1000
Xchants_pk[t][run] = float(Xchants_pk[t][run])/1000
Epidemic_ALL[t][run] = float(Epidemic_ALL[t][run]) / 1000
Epidemic_TV[t][run] = float(Epidemic_TV[t][run]) / 1000
Epidemic_LTE[t][run] = float(Epidemic_LTE[t][run]) / 1000
Epidemic_ISM[t][run] = float(Epidemic_ISM[t][run]) / 1000
Epidemic_CBRS[t][run] = float(Epidemic_CBRS[t][run]) / 1000
SprayNWait_ALL[t][run] = float(SprayNWait_ALL[t][run])/1000
HotPotato_ALL[t][run] = float(HotPotato_ALL[t][run])/1000
Xchants_mean = []
Xchants_SD = []
Xchants_mean_pk = []
Xchants_SD_pk = []
Epidemic_ALL_mean = []
Epidemic_ALL_SD = []
Epidemic_CBRS_mean = []
Epidemic_CBRS_SD = []
Epidemic_ISM_mean = []
Epidemic_ISM_SD = []
Epidemic_LTE_mean = []
Epidemic_LTE_SD = []
Epidemic_TV_mean = []
Epidemic_TV_SD = []
SprayNWait_ALL_mean = []
SprayNWait_ALL_SD = []
HotPotato_ALL_mean = []
HotPotato_ALL_SD = []
for i in range(len(Xchants)):
Xchants_mean.append(np.mean(Xchants[i]))
Xchants_SD.append(np.std(Xchants[i]))
Xchants_mean_pk.append(np.mean(Xchants_pk[i]))
Xchants_SD_pk.append(np.std(Xchants_pk[i]))
SprayNWait_ALL_mean.append(np.mean(SprayNWait_ALL[i]))
SprayNWait_ALL_SD.append(np.std(SprayNWait_ALL[i]))
HotPotato_ALL_mean.append(np.mean(HotPotato_ALL[i]))
HotPotato_ALL_SD.append(np.std(HotPotato_ALL[i]))
Epidemic_ALL_mean.append(np.mean(Epidemic_ALL[i]))
Epidemic_ALL_SD.append(np.std(Epidemic_ALL[i]))
Epidemic_CBRS_mean.append(np.mean(Epidemic_CBRS[i]))
Epidemic_CBRS_SD.append(np.std(Epidemic_CBRS[i]))
Epidemic_ISM_mean.append(np.mean(Epidemic_ISM[i]))
Epidemic_ISM_SD.append(np.std(Epidemic_ISM[i]))
Epidemic_LTE_mean.append(np.mean(Epidemic_LTE[i]))
Epidemic_LTE_SD.append(np.std(Epidemic_LTE[i]))
Epidemic_TV_mean.append(np.mean(Epidemic_TV[i]))
Epidemic_TV_SD.append(np.std(Epidemic_TV[i]))
x = np.array([x for x in range(1,11, 1)])
plt.xticks(fontsize=25)
plt.yticks(fontsize=25)
plt.xticks(np.arange(1, 11, 2))
# plt.xlim(0,12)
fig_name = "dummy.eps"
f
if p_id == 1:
plt.ylabel('Message delivery ratio', fontsize=25)
plt.xlabel('Number of data mules', fontsize=25)
plt.ylim(-0.05,1.25)
fig_name = "Plots/pdr_nodes_UMass.eps"
if p_id == 2:
plt.ylim(-1, 65)
plt.ylabel('Network delay (min)', fontsize=25)
plt.xlabel('Number of data mules', fontsize=25)
fig_name = "Plots/latency_nodes_UMass.eps"
if p_id == 3:
plt.ylabel('Energy expenditure (KJ)', fontsize=25)
plt.xlabel('Number of data mules', fontsize=25)
plt.ylim(-1, 13)
fig_name = "Plots/energy_nodes_UMass.eps"
if p_id == 4:
plt.ylabel('Message overhead', fontsize=25)
plt.xlabel('Number of data mules', fontsize=25)
plt.ylim(-1, 36)
fig_name = "Plots/overhead_nodes_UMass.eps"
plt.errorbar(x, Epidemic_CBRS_mean, 0, marker='h', markersize=10, linestyle='--', linewidth=3)
plt.errorbar(x, Epidemic_ISM_mean, 0, marker='p', markersize=10, linestyle='--', linewidth=3)
plt.errorbar(x, Epidemic_LTE_mean, 0, marker='v', markersize=10, linestyle='--', linewidth=3)
plt.errorbar(x, Epidemic_TV_mean, 0, marker='<', markersize=10, linestyle='--', linewidth=3)
plt.errorbar(x, Xchants_mean, 0, marker='D', markersize=10, linestyle='-', linewidth=3, color="#813a5c")
plt.errorbar(x, Xchants_mean_pk, 0, marker='o',markersize=10, linestyle='-', linewidth=3, color="#49FF00")
plt.errorbar(x, Epidemic_ALL_mean, 0, marker='>', markersize=10, linestyle='-', linewidth=3)
plt.errorbar(x, SprayNWait_ALL_mean, 0, marker='^', markersize=10, linestyle='-', linewidth=3)
plt.errorbar(x, HotPotato_ALL_mean, 0, marker='x', markersize=10, linestyle='-', linewidth=3)
if p_id == 1:
plt.legend([ "ER (CBRS)","ER (ISM)","ER (LTE)","ER (TV)","dDSAaR", "dDSAaR (Ideal)", "S-ER ","S-SnW ", "S-HP"], loc="center", bbox_to_anchor=(.5,0.9), fontsize=15, ncol = 3, frameon=False)
elif p_id == 2:
plt.legend([ "ER (CBRS)","ER (ISM)","ER (LTE)","ER (TV)","dDSAaR", "dDSAaR (Ideal)", "S-ER ","S-SnW ", "S-HP"], loc="lower center", fontsize=15, ncol = 3, frameon=False)
elif p_id ==3:
plt.legend([ "ER (CBRS)","ER (ISM)","ER (LTE)","ER (TV)","dDSAaR", "dDSAaR (Ideal)", "S-ER ","S-SnW ", "S-HP"], loc="upper left", fontsize=15, ncol = 3, frameon=False)
elif p_id ==4:
plt.legend([ "ER (CBRS)","ER (ISM)","ER (LTE)","ER (TV)","dDSAaR", "dDSAaR (Ideal)", "S-ER ","S-SnW ", "S-HP"], loc="upper left", fontsize=15, ncol = 3, frameon=False)
plt.tight_layout()
plt.savefig(fig_name)
plt.show()